About Output of the Keras LSTM

Question

I have a built a LSTM architecture using Keras. My goal is to map length 29 time series input sequences of floats to length 29 output sequences of floats. I am trying to implement a "many-to-many" approach. I followed this post for implementing such a model.

I start by reshaping each data point into an np.array of shape `(1, 29, 1). I have multiple data points and train the model on each one separately. The following code is how I build my model:

def build_model():
    # define model
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.LSTM(29, return_sequences=True, input_shape=(29, 1)))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.3))

    model.compile(optimizer='sgd', loss='mse', metrics = ['mae'])

    #cast data
    for point in train_dict:
        train_data = train_dict[point]

        train_dataset = tf.data.Dataset.from_tensor_slices((
            tf.cast(train_data[0], features_type),
            tf.cast(train_data[1], target_type))
        ).repeat() #cast into X, Y

        # fit model


        model.fit(train_dataset, epochs=100,steps_per_epoch = 1,verbose=0)


        print(model.summary())   
    return model 

I am confused because when I call model.predict(test_point, steps = 1, verbose = 1) the model returns 29 length 29 sequences! I don't understand why this is happening, based on my understanding from the linked post. When I try return_state=True instead of return_sequences=True then my code raises this error: ValueError: All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API. ValueError: All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.

How do I solve the problem?

Answer 1

Your model has few flaws.

1. The last layer of your model is an LSTM. Assuming you're doing either classification / regression. This should be followed by a Dense layer (SoftMax/sigmoid - classification, linear - regression). But since this is a time-series problem, dense layer should be wrapped in a TimeDistributed wrapper.

2. It's odd to apply a LeakyReLU on top of the LSTM.

I've fixed the code with fixes for above issues. See if that helps.

from tensorflow.keras.layers import Embedding, Input, Bidirectional, LSTM, Dense, Concatenate, LeakyReLU, TimeDistributed
from tensorflow.keras.initializers import Constant
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
def build_model():
    # define model
    model = Sequential()
    model.add(LSTM(29, return_sequences=True, input_shape=(29, 1)))
    model.add(TimeDistributed(Dense(1)))
    model.compile(optimizer='sgd', loss='mse', metrics = ['mae'])


    print(model.summary())   
    return model 

model = build_model()